Data Quality: Empowering Businesses with Analytics and AI (Hardcover)
暫譯: 數據品質：利用分析與人工智慧賦能企業 (精裝版)
Southekal, Prashanth
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商品描述
Discover how to achieve business goals by relying on high-quality, robust data
In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications.
The author shows you how to:
- Profile for data quality, including the appropriate techniques, criteria, and KPIs
- Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.
- Formulate the reference architecture for data quality, including practical design patterns for remediating data quality
- Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business
An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
商品描述(中文翻譯)
**發現如何依賴高品質、穩健的數據來實現商業目標**
在《數據質量：利用分析和人工智慧賦能企業》中，資深數據與分析專業人士提供了一個實用且動手操作的討論，說明如何利用高品質數據加速商業成果。在這本書中，您將學習定義和評估數據質量的技術，了解如何確保您公司的數據收集實踐避免常見的陷阱和缺陷，提高業務中的數據質量水平，並保證所產生的數據對於推動高級分析和人工智慧應用是有用的。
作者將向您展示如何：
- 進行數據質量分析，包括適當的技術、標準和關鍵績效指標（KPI）
- 確定業務中數據質量問題的根本原因，並討論16個常見的根本原因，這些原因會降低組織中的數據質量
- 制定數據質量的參考架構，包括修正數據質量的實用設計模式
- 實施10個最佳數據質量實踐及改善業務運營、合規性和決策能力所需的能力
《數據質量：利用分析和人工智慧賦能企業》是數據科學家、數據分析師、商業智慧專業人士、首席技術官和數據官以及任何對收集和使用高品質數據有興趣的人士的重要資源，這本書也將成為對了解穩健數據與其他數據之間區別感興趣的商業領導者書架上的一部分。
作者簡介
PRASHANTH SOUTHEKAL, PHD, is a data, analytics, and AI consultant, author, and professor. He has worked and consulted for over 80 organizations including P&G, GE, Shell, Apple, FedEx, and SAP. Dr. Southekal is the author of Data for Business Performance and Analytics Best Practices (ranked #1 analytics books of all time by BookAuthority) and writes regularly on data, analytics, and AI in Forbes and CFO.University. He serves on the Editorial Board of MIT CDOIQ Symposium and is an advisory board member at BGV (Benhamou Global Ventures) a Silicon Valley-based venture capital firm. Apart from his consulting and advisory pursuits, he has trained over 3,000 professionals worldwide in data and analytics. Dr. Southekal is also an adjunct professor of data and analytics at IE Business School (Madrid, Spain). CDO Magazine included him in the top 75 global academic data leaders of 2022. He holds a PhD from ESC Lille (FR), an MBA from the Kellogg School of Management (US), and holds the ICD.D designation from the Institute of Corporate Directors (Canada).
作者簡介(中文翻譯)
PRASHANTH SOUTHEKAL, PHD, 是一位數據、分析和人工智慧顧問、作者及教授。他曾為超過80個組織提供服務和諮詢，包括P&G、GE、Shell、Apple、FedEx和SAP。Southekal博士是《Data for Business Performance》和《Analytics Best Practices》的作者（這本書被BookAuthority評選為有史以來排名第一的分析書籍），並定期在《Forbes》和CFO.University上撰寫有關數據、分析和人工智慧的文章。他擔任MIT CDOIQ Symposium的編輯委員會成員，並且是位於矽谷的風險投資公司BGV（Benhamou Global Ventures）的顧問委員會成員。除了顧問和諮詢工作外，他還在全球培訓了超過3,000名專業人士，專注於數據和分析。Southekal博士同時也是IE商學院（西班牙馬德里）的數據和分析兼任教授。《CDO Magazine》將他列入2022年全球75位學術數據領導者名單。他擁有法國里爾商學院的博士學位（ESC Lille），美國凱洛格管理學院的MBA學位，並持有加拿大企業董事協會的ICD.D資格。
